gemseo.mlearning.regression.algos.random_forest_settings module

Settings of the multiLayer perceptron (MLP).

Settings RandomForestRegressor_Settings(*, transformer=None, parameters=None, input_names=(), output_names=(), n_estimators=100, random_state=0)

Bases: BaseRegressorSettings

The settings of the multiLayer perceptron (MLP).

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

Parameters:

• transformer (Mapping[str, Any])

• parameters (Mapping[str, Any])

• input_names (Sequence[str]) --

  By default it is set to ().

• output_names (Sequence[str]) --

  By default it is set to ().

• n_estimators (Annotated[int, Gt(gt=0)]) --

  By default it is set to 100.

• random_state (Annotated[int, Ge(ge=0)] | None) --

  By default it is set to 0.

Return type:

None

n_estimators: PositiveInt = 100

The number of trees in the forest.

Constraints:

• gt = 0

random_state: NonNegativeInt | None = 0

The random state parameter.

If None, use the global random state instance from numpy.random. Creating the model multiple times will produce different results. If int, use a new random number generator seeded by this integer. This will produce the same results.